This article updates our perspectives on when the coronavirus pandemic will end to reflect the latest information on vaccine rollout, variants of concern, and disease progression. In the United Kingdom and the United States, we see progress toward a transition to normalcy during the second quarter of 2021. The new wave of cases in the European Union means that a similar transition is likely to come later there, in the late second or third quarter. Improved vaccine availability makes herd immunity most likely in the third quarter for the United Kingdom and the United States and in the fourth quarter for the European Union, but risks threaten that timeline. The timeline in other countries will depend on seven crucial variables. And when herd immunity is reached, the risks will not vanish; herd immunity may prove temporary or be limited to regions in a country.
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Lambiotte, R & Schaub, M
Complex networks are typically not homogeneous, as they tend to display an array of structures at different scales. A feature that has attracted a lot of research is their modular organisation, i.e., networks may often be considered as being composed of certain building blocks, or modules. In this book, we discuss a number of ways in which this idea of modularity can be conceptualised, focusing specifically on the interplay between modular network structure and dynamics taking place on a network. We discuss, in particular, how modular structure and symmetries may impact on network dynamics and, vice versa, how observations of such dynamics may be used to infer the modular structure. We also revisit several other notions of modularity that have been proposed for complex networks and show how these can be related to and interpreted from the point of view of dynamical processes on networks. Several references and pointers for further discussion and future work should inform practitioners and researchers, and may motivate further studies in this area at the core of Network Science.
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Percolation theory illuminates the behavior of many kinds of networks, from cell-phone connections to disease transmission.
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Carlos Gershenson, Daniel Polani and Georg Martius
Front. Robot. AI, 26 March 2021
Complexity occurs when relevant interactions prevent the study of elements of a system in isolation. These interactions between elements may lead to the self-organization of the system. A system can be described as self-organizing when its global properties are a product of the interactions of its components. Complexity and self-organization are prevalent in a broad variety of systems. Because of this, they have been studied from multiple perspectives and disciplines, leading naturally to transdisciplinary studies.
The scientific study of complexity and self-organization was limited before the popularization of computers in the 1980s, as previous tools were insufficient to deal with hundreds or thousands of variables. Thus, computer science has been essential for these studies.
In computational intelligence, complexity and self-organization have been studied and exploited with different purposes. The aim of this Research Topic was to bring together novel research into a coherent collection, spanning from theory and methods to simulations and applications.
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Most modeling efforts during the COVID-19 pandemic have sought to address urgent practical concerns. But some groups aim to bolster the theoretical underpinnings of that work instead.
Researchers can’t directly observe many key features of disease transmission. As a result, they rely on statistical models to translate what they can see to what they want to know. But they’re finding that for COVID-19 in particular, some of these methods have been giving them the wrong answers.
Read the full article at: www.quantamagazine.org